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| import torch | |
| import torch.nn as nn | |
| import torchvision.transforms.functional as TF | |
| class DoubleConv(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| super().__init__() | |
| self.conv = nn.Sequential( | |
| nn.Conv2d(in_channels, out_channels, 3, padding=1, bias=False), | |
| nn.BatchNorm2d(out_channels), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False), | |
| nn.BatchNorm2d(out_channels), | |
| nn.ReLU(inplace=True) | |
| ) | |
| def forward(self, x): | |
| return self.conv(x) | |
| class UNet(nn.Module): | |
| def __init__(self, in_channels=3, out_channels=1, features=[64, 128, 256, 512]): | |
| super().__init__() | |
| self.ups = nn.ModuleList() | |
| self.downs = nn.ModuleList() | |
| self.pool = nn.MaxPool2d(kernel_size=2, stride=2) | |
| # Down part of UNet | |
| for feature in features: | |
| self.downs.append(DoubleConv(in_channels, feature)) | |
| in_channels = feature | |
| # Up part of UNet | |
| for feature in reversed(features): | |
| self.ups.append( | |
| nn.ConvTranspose2d(feature * 2, feature, kernel_size=2, stride=2) | |
| ) | |
| self.ups.append(DoubleConv(feature * 2, feature)) | |
| self.bottleneck = DoubleConv(features[-1], features[-1] * 2) | |
| self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1) | |
| self.dropout = nn.Dropout2d(p=0.2) | |
| def forward(self, x): | |
| skip_connections = [] | |
| for down in self.downs: | |
| x = down(x) | |
| skip_connections.append(x) | |
| x = self.pool(x) | |
| x = self.bottleneck(x) | |
| skip_connections = skip_connections[::-1] | |
| for idx in range(0, len(self.ups), 2): | |
| x = self.ups[idx](x) | |
| skip_connection = skip_connections[idx // 2] | |
| if x.shape != skip_connection.shape: | |
| x = TF.resize(x, size=skip_connection.shape[2:], antialias=True) | |
| concat_skip = torch.cat((skip_connection, x), dim=1) | |
| x = self.ups[idx + 1](concat_skip) | |
| x = self.dropout(x) | |
| return self.final_conv(x) |